Skip to main content

Advertisement

SpringerLink
Log in

Techno-economic assessment of steel plant participation in DSM programs (case study: Iran’s industrial operational reserve program)

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

Industrial sector in each country consumes large amounts of total produced electrical energy. Hence, industrial customers with flexible loads are attractive targets for implementing demand-side management (DSM) programs. In recent years, Iranian energy ministry has applied an industrial operational reserve program (IORP) to reduce energy shortage during peak hours. This program has been developed to encourage industrial customers to participate in DSM programs, while steel plants show fewer tendencies to take part in these programs. In this paper, a techno-economic feasibility analysis of implementing IORP on steel plants, as the largest consumers of Iran’s industrial sector, is presented. For this purpose, first, the production process of a steel plant is modeled. Then, an optimization problem is developed to obtain the optimal scheduling of processes involved in producing steel through considering their technical and economic aspects. In order to solve the proposed problem which is mixed-integer nonlinear, Benders decomposition approach is used. Eventually, from the viewpoint of a steel plant owner, the profitability of taking part in IORP is investigated in three possible scenarios. The simulation results showed that steel plants can benefit from participating in IORP.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Abdelaziz, E. A., Saidur, R., & Mekhilef, S. (2011). A review on energy saving strategies in industrial sector. Renewable and Sustainable Energy Reviews, 15(1), 150–168.

    Article  Google Scholar 

  • Alcázar-Ortega, M., Álvarez-Bel, C., Domijan, A., & Escrivá-Escrivá, G. (2012). Economic and environmental evaluation of customers' flexibility participating in operation markets: application to the meat industry. Energy, 41(1), 368–379.

    Article  Google Scholar 

  • Ashok, S., & Banerjee, R. (2000). Load-management applications for the industrial sector. Applied Energy, 66(2), 105–111.

    Article  Google Scholar 

  • Basnet, S. M., Aburub, H., & Jewell, W. (2019). Residential demand response program: predictive analytics, virtual storage model and its optimization. Journal of Energy Storage, 23, 183–194.

    Article  Google Scholar 

  • Carrasqueira, P., Alves, M. J., & Antunes, C. H. (2017). Bi-level particle swarm optimization and evolutionary algorithm approaches for residential demand response with different user profiles. Information Sciences, 418, 405–420.

    Article  Google Scholar 

  • Castro, P. M., Sun, L., & Harjunkoski, I. (2013). Resource–task network formulations for industrial demand side management of a steel plant. Industrial & Engineering Chemistry Research, 52(36), 13046–13058.

    Article  Google Scholar 

  • DSM Office of Iran’s Energy Ministry (2017) Industrial Operational Reserve Program www.eepdc.ir/dorsapax/userfiles/file/zakhireh.pdf

  • Finn, P., & Fitzpatrick, C. (2014). Demand side management of industrial electricity consumption: promoting the use of renewable energy through real-time pricing. Applied Energy, 113, 11–21.

    Article  Google Scholar 

  • Gholian, A., Rad, H. M., & Hua, Y. (2016). Optimal industrial load control in smart grid. IEEE Transactions on Smart Grid, 7(5), 2305–2316.

    Article  Google Scholar 

  • Henríquez, R., Wenzel, G., Olivares, D. E., & Negrete-Pincetic, M. (2017). Participation of demand response aggregators in electricity markets: optimal portfolio management. IEEE Transactions on Smart Grid, 9(5), 4861–4871.

    Article  Google Scholar 

  • Hu, M., & Xiao, F. (2018). Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm. Applied Energy, 219, 151–164.

    Article  Google Scholar 

  • Huang, X., Hong, S. H., Yu, M., Ding, Y., & Jiang, J. (2019). Demand response management for industrial facilities: a deep reinforcement learning approach. IEEE Access, 7, 82194–82205.

    Article  Google Scholar 

  • Jordehi, A. R. (2019). Optimisation of demand response in electric power systems, a review. Renewable and Sustainable Energy Reviews, 103, 308–319.

    Article  Google Scholar 

  • Karimi, H., Jadid, S., & Saboori, H. (2019). Multi-objective bi-level optimisation to design real-time pricing for demand response programs in retail markets. IET Generation Transmission and Distribution, 13(8), 1287–1296.

    Article  Google Scholar 

  • Klobasa, M. (2010). Analysis of demand response and wind integration in Germany's electricity market. IET Renewable Power Generation, 4(1), 55–63.

    Article  Google Scholar 

  • Mazidi, M., Zakariazadeh, A., Jadid, S., & Siano, P. (2014). Integrated scheduling of renewable generation and demand response programs in a microgrid. Energy Conversion and Management, 86, 1118–1127.

    Article  Google Scholar 

  • Nwulu, N. I., & Xia, X. (2017). Optimal dispatch for a microgrid incorporating renewables and demand response. Renewable Energy, 101, 16–28.

    Article  Google Scholar 

  • Pallonetto, F., De Rosa, M., Milano, F., & Finn, D. P. (2019). Demand response algorithms for smart-grid ready residential buildings using machine learning models. Applied Energy, 239, 1265–1282.

    Article  Google Scholar 

  • Paulus, M., & Borggrefe, F. (2011). The potential of demand-side management in energy-intensive industries for electricity markets in Germany. Applied Energy, 88(2), 432–441.

    Article  Google Scholar 

  • Saebi, J. and Nguyen, D.T. (2020). Distributed demand response market model for facilitating wind power integration. IET Smart Grid.

  • Sedighizadeh, M., Alavi, S. M., & Mohammadpour, A. (2020). Stochastic optimal scheduling of microgrids considering demand response and commercial parking lot by AUGMECON method. Iranian Journal of Electrical and Electronic Engineering (IJEEE) in press.

  • Shafie-Khah, M., & Siano, P. (2017). A stochastic home energy management system considering satisfaction cost and response fatigue. IEEE Transactions on Industrial Informatics, 14(2), 629–638.

    Article  Google Scholar 

  • Shahidehpour, M., & Fu, Y. (2005). Benders decomposition: applying Benders decomposition to power systems. IEEE Power and Energy Magazine, 3(2), 20–21.

    Article  Google Scholar 

  • Tavanir Company (2017). Detailed statistics of Iran's electricity industry. http://amar.tavanir.org.ir/en/

  • Thollander, P., & Ottosson, M. (2010). Energy management practices in Swedish energy-intensive industries. Journal of Cleaner Production, 18(12), 1125–1133.

    Article  Google Scholar 

  • Wang, Z., Paranjape, R., Chen, Z., & Zeng, K. (2019). Multi-agent optimization for residential demand response under real-time pricing. Energies, 12(15), 2867.

    Article  Google Scholar 

  • Xenos, D. P., Noor, I. M., Matloubi, M., Cicciotti, M., Haugen, T., & Thornhill, N. F. (2016). Demand-side management and optimal operation of industrial electricity consumers: an example of an energy-intensive chemical plant. Applied Energy, 182, 418–433.

    Article  Google Scholar 

  • Zhang X (2017) Approaches to enable demand response by industrial loads for ancillary services provision, Dissertation, University of Carnegie Mellon.

  • Zhang, X., & Hug, G. (2014). Optimal regulation provision by aluminum smelters (pp. 1–5). IEEE Power and Energy Society General Meeting.

  • Zhang, Q., Morari, M. F., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2016). An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load. Computers & Chemical Engineering, 86, 106–119.

    Article  Google Scholar 

  • Zhang, X., Hug, G., & Harjunkoski, I. (2017a). Cost-effective scheduling of steel plants with flexible EAFs. IEEE Transactions on Smart Grid, 8(1), 239–249.

    Article  Google Scholar 

  • Zhang, X., Hug, G., Kolter, J. Z., & Harjunkoski, I. (2017b). Demand response of ancillary service from industrial loads coordinated with energy storage. IEEE Transactions on Power Systems, 33(1), 951–961.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Bojnourd Branch, Islamic Azad University, Bojnourd, Iran

    Saeed SeyyedMahdavi

  2. Department of Electrical Engineering, Faculty of Engineering, University of Bojnord, Bojnurd, Iran

    Javad Saebi

Authors
  1. Saeed SeyyedMahdavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javad Saebi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saeed SeyyedMahdavi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

SeyyedMahdavi, S., Saebi, J. Techno-economic assessment of steel plant participation in DSM programs (case study: Iran’s industrial operational reserve program). Energy Efficiency 13, 1315–1328 (2020). https://doi.org/10.1007/s12053-020-09886-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-020-09886-0

Keywords

Search

Navigation